#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 26 14:46:56 2017
@author: luohao
"""

"""
CVPR2017 paper:Zhong Z, Zheng L, Cao D, et al. Re-ranking Person Re-identification with k-reciprocal Encoding[J]. 2017.
url:http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhong_Re-Ranking_Person_Re-Identification_CVPR_2017_paper.pdf
Matlab version: https://github.com/zhunzhong07/person-re-ranking
"""

"""
Modified by L.Song and C.Wang
"""

import numpy as np
from scipy.spatial.distance import cdist

def re_ranking(input_feature_source, input_feature, k1=20, k2=6, lambda_value=0.1):

    all_num = input_feature.shape[0]    
    feat = input_feature.astype(np.float16)

    if lambda_value != 0:
        print('Computing source distance...')
        all_num_source  = input_feature_source.shape[0]
        sour_tar_dist = np.power(
            cdist(input_feature, input_feature_source), 2).astype(np.float16)
        sour_tar_dist = 1-np.exp(-sour_tar_dist)
        source_dist_vec = np.min(sour_tar_dist, axis = 1)
        source_dist_vec = source_dist_vec / np.max(source_dist_vec)
        source_dist = np.zeros([all_num, all_num])
        for i in range(all_num):
            source_dist[i, :] = source_dist_vec + source_dist_vec[i]
        del sour_tar_dist
        del source_dist_vec

    print('Computing original distance...')
    original_dist = cdist(feat,feat).astype(np.float16)  
    original_dist = np.power(original_dist,2).astype(np.float16)
    del feat    
    euclidean_dist = original_dist
    gallery_num = original_dist.shape[0] #gallery_num=all_num
    original_dist = np.transpose(original_dist/np.max(original_dist,axis = 0))
    V = np.zeros_like(original_dist).astype(np.float16)
    initial_rank = np.argsort(original_dist).astype(np.int32)  ## default axis=-1.  

    print('Starting re_ranking...')
    for i in range(all_num):
        # k-reciprocal neighbors
        forward_k_neigh_index = initial_rank[i,:k1+1]  ## k1+1 because self always ranks first. forward_k_neigh_index.shape=[k1+1].  forward_k_neigh_index[0] == i.
        backward_k_neigh_index = initial_rank[forward_k_neigh_index,:k1+1]  ##backward.shape = [k1+1, k1+1]. For each ele in forward_k_neigh_index, find its rank k1 neighbors
        fi = np.where(backward_k_neigh_index==i)[0]  
        k_reciprocal_index = forward_k_neigh_index[fi]   ## get R(p,k) in the paper
        k_reciprocal_expansion_index = k_reciprocal_index
        for j in range(len(k_reciprocal_index)):
            candidate = k_reciprocal_index[j]
            candidate_forward_k_neigh_index = initial_rank[candidate,:int(np.around(k1/2))+1]
            candidate_backward_k_neigh_index = initial_rank[candidate_forward_k_neigh_index,:int(np.around(k1/2))+1]
            fi_candidate = np.where(candidate_backward_k_neigh_index == candidate)[0]
            candidate_k_reciprocal_index = candidate_forward_k_neigh_index[fi_candidate]
            if len(np.intersect1d(candidate_k_reciprocal_index,k_reciprocal_index))> 2/3*len(candidate_k_reciprocal_index):
                k_reciprocal_expansion_index = np.append(k_reciprocal_expansion_index,candidate_k_reciprocal_index)

        k_reciprocal_expansion_index = np.unique(k_reciprocal_expansion_index)  ## element-wise unique
        weight = np.exp(-original_dist[i,k_reciprocal_expansion_index])  
        V[i,k_reciprocal_expansion_index] = weight/np.sum(weight)
    #original_dist = original_dist[:query_num,]    
    if k2 != 1:
        V_qe = np.zeros_like(V,dtype=np.float16)
        for i in range(all_num):
            V_qe[i,:] = np.mean(V[initial_rank[i,:k2],:],axis=0)
        V = V_qe
        del V_qe
    del initial_rank
    invIndex = [] 
    for i in range(gallery_num):
        invIndex.append(np.where(V[:,i] != 0)[0])  #len(invIndex)=all_num

    jaccard_dist = np.zeros_like(original_dist,dtype = np.float16)


    for i in range(all_num):
        temp_min = np.zeros(shape=[1,gallery_num],dtype=np.float16)
        indNonZero = np.where(V[i,:] != 0)[0]
        indImages = []
        indImages = [invIndex[ind] for ind in indNonZero]
        for j in range(len(indNonZero)):
            temp_min[0,indImages[j]] = temp_min[0,indImages[j]]+ np.minimum(V[i,indNonZero[j]],V[indImages[j],indNonZero[j]])
        jaccard_dist[i] = 1-temp_min/(2-temp_min)

    pos_bool = (jaccard_dist < 0)
    jaccard_dist[pos_bool] = 0.0

    if lambda_value == 0:
        return jaccard_dist
    else:
        final_dist = jaccard_dist*(1-lambda_value) + source_dist*lambda_value
        return final_dist
